235 citations · 783 across the 25 of their papers we have counts for
22 papers · 1 filter
Numerical simulation of D-Wave's quantum advantage experiment with time-dependent variational Monte Carlo
Roeland Wiersema
Programmable quantum annealers can realize real-time dynamics of frustrated transverse-field Ising models on large, nontrivial graphs. Recent work by King et al. argued that the cl…
Pulse Quality Optimisation in Quantum Optimal Control
Dylan Lewis, Roeland Wiersema
Quantum optimal control methods are widely used to design experimental control pulses such as laser amplitudes, phases, or detunings, that implement a target unitary evolution. In…
Hessian-vector products for tensor networks via recursive tangent-state propagation
Isabel Nha Minh Le, Roeland Wiersema, Christian B. Mendl
Optimizing tensor networks with standard first-order methods often leads to slow convergence and entrapment in local minima. Although second-order optimization offers enhanced robu…
Neural Quantum States in Mixed Precision
Massimo Solinas, Agnes Valenti, Nawaf Bou-Rabee +1
Scientific computing has long relied on double precision (64-bit floating point) arithmetic to guarantee accuracy in simulations of real-world phenomena. However, the growing avail…
Entanglement and optimization within autoregressive neural quantum states
Andrew Jreissaty, Hang Zhang, Jairo C. Quijano +2
Neural quantum states (NQSs) are powerful variational ansätze capable of representing highly entangled quantum many-body wavefunctions. While the average entanglement properties of…
Quantum Optimal Control with Geodesic Pulse Engineering
Dylan Lewis, Roeland Wiersema, Sougato Bose
Designing multi-qubit quantum logic gates with experimental constraints is an important problem in quantum computing. Here, we develop a new quantum optimal control algorithm for f…
Circuit compression for 2D quantum dynamics
Matteo D'Anna, Yuxuan Zhang, Roeland Wiersema +2
The study of out-of-equilibrium quantum many-body dynamics remains one of the most exciting research frontiers of physics, standing at the crossroads of our understanding of comple…
Classification of dynamical Lie algebras generated by spin interactions on undirected graphs
Efekan Kökcü, Roeland Wiersema, Alexander F. Kemper +1
We provide a classification of all dynamical Lie algebras generated by 2-local spin interactions on undirected graphs. Building on our previous work where we provided such a classi…
Scalable quantum dynamics compilation via quantum machine learning
Yuxuan Zhang, Roeland Wiersema, Juan Carrasquilla +2
Quantum dynamics compilation is an important task for improving quantum simulation efficiency: It aims to synthesize multi-qubit target dynamics into a circuit consisting of as few…
Geometric Quantum Machine Learning with Horizontal Quantum Gates
Roeland Wiersema, Alexander F. Kemper, Bojko N. Bakalov +1
In the current framework of Geometric Quantum Machine Learning, the canonical method for constructing a variational ansatz that respects the symmetry of some group action is by for…
Beyond-classical computation in quantum simulation
Andrew D. King, Alberto Nocera, Marek M. Rams +60
Quantum computers hold the promise of solving certain problems that lie beyond the reach of conventional computers. However, establishing this capability, especially for impactful…
Geodesic Algorithm for Unitary Gate Design with Time-Independent Hamiltonians
Dylan Lewis, Roeland Wiersema, Juan Carrasquilla +1
Larger multi-qubit quantum gates allow shallower, more efficient quantum circuits, which could decrease the prohibitive effect of noise on algorithms for noisy intermediate-scale q…
Classification of dynamical Lie algebras for translation-invariant 2-local spin systems in one dimension
Roeland Wiersema, Efekan Kökcü, Alexander F. Kemper +1
Much is understood about 1-dimensional spin chains in terms of entanglement properties, physical phases, and integrability. However, the Lie algebraic properties of the Hamiltonian…
Here comes the SU(N): multivariate quantum gates and gradients
Roeland Wiersema, Dylan Lewis, David Wierichs +2
Variational quantum algorithms use non-convex optimization methods to find the optimal parameters for a parametrized quantum circuit in order to solve a computational problem. The…
Circuit connectivity boosts by quantum-classical-quantum interfaces
Roeland Wiersema, Leonardo Guerini, Juan Felipe Carrasquilla +1
High-connectivity circuits are a major roadblock for current quantum hardware. We propose a hybrid classical-quantum algorithm to simulate such circuits without swap-gate ladders.…
Optimizing quantum circuits with Riemannian gradient flow
Roeland Wiersema, Nathan Killoran
Variational quantum algorithms are a promising class of algorithms that can be performed on currently available quantum computers. In most settings, the free parameters of a variat…
Quasiprobabilistic state-overlap estimator for NISQ devices
Leonardo Guerini, Roeland Wiersema, Juan Felipe Carrasquilla +1
As quantum technologies mature, the development of tools for benchmarking their ability to prepare and manipulate complex quantum states becomes increasingly necessary. A key conce…
Measurement-induced entanglement phase transitions in variational quantum circuits
Roeland Wiersema, Cunlu Zhou, Juan Felipe Carrasquilla +1
Variational quantum algorithms (VQAs), which classically optimize a parametrized quantum circuit to solve a computational task, promise to advance our understanding of quantum many…
Exploring entanglement and optimization within the Hamiltonian Variational Ansatz
Roeland Wiersema, Cunlu Zhou, Yvette de Sereville +3
Quantum variational algorithms are one of the most promising applications of near-term quantum computers; however, recent studies have demonstrated that unless the variational quan…
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
Michael Broughton, Guillaume Verdon, Trevor McCourt +26
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high…
Implementing perceptron models with qubits
Roeland Wiersema, H. J. Kappen
We propose a method for learning a quantum probabilistic model of a perceptron. By considering a cross entropy between two density matrices we can learn a model that takes noisy ou…
PennyLane: Automatic differentiation of hybrid quantum-classical computations
Ville Bergholm, Josh Izaac, Maria Schuld +65
PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices,…